Iris recognition using consistency information

ABSTRACT

Embodiments of the present invention include but are not limited to methods and systems for iris recognition. An iris recognition method may comprise comparing a plurality of images of an iris to determine at least one of one or more consistent features and one or more inconsistent features of the iris; and constructing an enrollment template for the iris based at least in part on the at least one of the one or more consistent features and the one or more inconsistent features.

TECHNICAL FIELD

Embodiments of the invention relate generally to the field ofbiometrics, specifically to methods, apparatuses, and systems associatedwith iris recognition.

BACKGROUND

Biometric methods have gained tremendous interest as a means forreliably verifying the identity of a person. Many current identificationsystems are limited to identification cards, passwords, or personalidentification numbers for verifying the identity of a person, but thesemethods have proven to be less than desirable due to theirtransferability. Biometric methods, on the other hand, identify a personbased on some physical or behavioral characteristic, which generallycannot be transferred or otherwise misplaced.

With regard to iris biometrics in particular, the highly-varied textureof the human iris has spurred interest in using iris recognition as abiometric means for identifying a person. Despite the advances in irisrecognition systems, unfortunately, significant shortcomings exist. Forexample, certain areas of the iris may provide less consistentinformation, which may lead to unacceptable verification outcomes interms of acceptance and rejection of identity claims. Accordingly, amore reliable system of iris recognition is of substantial importance.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments of the present invention will be readily understood by thefollowing detailed description in conjunction with the accompanyingdrawings. Embodiments of the invention are illustrated by way of exampleand not by way of limitation in the figures of the accompanyingdrawings.

FIG. 1 schematically illustrates an image of an eye;

FIG. 2 is a flow diagram of an iris recognition enrollment method inaccordance with various embodiments of the present invention;

FIG. 3 depicts exemplary inconsistent regions of iris feature vectors offive different test subjects constructed using an iris recognitionenrollment method in accordance with various embodiments of the presentinvention;

FIG. 4 depicts exemplary inconsistent regions of an iris feature vectormasked according to varying consistency thresholds using an irisrecognition method in accordance with various embodiments of the presentinvention;

FIG. 5 is a flow diagram of an iris recognition method in accordancewith various embodiments of the present invention;

FIG. 6 is a flow diagram of another iris recognition method inaccordance with various embodiments of the present invention;

FIG. 7 is a block diagram of an iris recognition apparatus in accordancewith various embodiments of the present invention; and

FIG. 8 is a block diagram of an article of manufacture for implementingan iris recognition method in accordance with various embodiments of thepresent invention.

DETAILED DESCRIPTION OF EMBODIMENTS OF THE INVENTION

In the following detailed description, reference is made to theaccompanying drawings which form a part hereof and in which is shown byway of illustration embodiments in which the invention may be practiced.It is to be understood that other embodiments may be utilized andstructural or logical changes may be made without departing from thescope of the present invention. Therefore, the following detaileddescription is not to be taken in a limiting sense, and the scope ofembodiments in accordance with the present invention is defined by theappended claims and their equivalents.

Various operations may be described as multiple discrete operations inturn, in a manner that may be helpful in understanding embodiments ofthe present invention; however, the order of description should not beconstrued to imply that these operations are order dependent.

The description may use perspective-based descriptions such as up/down,back/front, and top/bottom. Such descriptions are merely used tofacilitate the discussion and are not intended to restrict theapplication of embodiments of the present invention.

The description may use the phrases “in an embodiment,” or “inembodiments,” which may each refer to one or more of the same ordifferent embodiments. Furthermore, the terms “comprising,” “including,”“having,” and the like, as used with respect to embodiments of thepresent invention, are synonymous.

A phrase in the form of “NB” means “A or B.” A phrase in the form “Aand/or B” means “(A), (B), or (A and B).” A phrase in the form “at leastone of A, B and C” means “(A), (B), (C), (A and B), (A and C), (B and C)or (A, B and C).” A phrase in the form “(A) B” means “(B) or (A B),”that is, A is optional.

Various embodiments of the present invention are related to irisrecognition, and methods, apparatuses, and systems for iris recognition.An article of manufacture may be adapted to perform various disclosedmethods, and a computing system may be endowed with one or morecomponents of the disclosed articles of manufacture and/or systems andmay be employed to perform one or more methods as disclosed herein.

According to various embodiments, a biometric method may comprise anynumber of operations including, for example, one or more ofcharacteristic acquisition (such as, for example, image acquisition),enrollment template creation, identification, and authentication.“Enrollment” generally refers to the sampling of biometric informationof a system user and creation therefrom of an enrollment template. Theenrollment template may be invoked or created during an authenticationoperation for verifying the identity of the system user. Duringauthentication, the enrollment template may be compared to a sample toverify that the claimed identity is true. “Authentication” is sometimesalternately referred to in the art as any one or more of comparison,matching, and verification. Instead of or in addition to anauthentication operation, the enrollment template may be invoked orcreated during an identification operation for identifying an unknownperson. During identification, a sample may be acquired from the unknownperson and matched against one or more enrolled persons to identify theunknown person.

With respect to biometric methods using the human iris as the biometrictrait, it has been observed that some textural features of a human iris,or some representation of the textural features, may not necessarily beconsistent. In general, “inconsistency” may refer to a particularfeature, or representation/indication of a feature, having someprobability of differing between images of the same iris. Inconsistencymay be a result of specific physical features of the iris, or may bedeveloped during an acquisition or enrollment template creationoperation. For example, when an image of the eye is taken, the resultingimage may be a discretized image of an original consistent signal. Ifthe physical eye is moved one-half pixel, for instance, in onedirection, the resulting discretized image may be different.Inconsistencies may also arise if the head is tilted at different anglesat different times. Still further, a segmentation algorithm (i.e., onethat finds the iris and pupil in the image) may misestimate the locationof the iris and/or the pupil, which may result in an inconsistency.

In any event, failure to account for such inconsistencies may have theresult of unacceptable verification outcomes in terms of acceptance andrejection of identity claims. For example, a false acceptance or falserejection of an identity claim may occur. In identification schemes,failure to account for inconsistencies may result in unacceptableidentification outcomes in terms of misidentification ornon-identification.

For various embodiments of the present invention, an iris recognitionmethod may comprise acquiring a plurality of images of an iris,comparing the plurality of images to determine one or more consistentfeatures and one or more inconsistent features of the iris, andconstructing an enrollment template for the iris based at least in parton the one or more consistent features and the one or more inconsistentfeatures.

Some generally-known features of an eye are discussed herein. Forreference, these features are also illustrated in FIG. 1. Asillustrated, an eye generally includes, but is not limited to, an iris2, a pupil 4, a pupillary boundary 6, and a limbic boundary 8.

Turning now to FIG. 2, illustrated is a flow diagram of a portion of theoperations associated with constructing an enrollment template, whichmay be invoked during an authentication operation for verifying theidentity of a subject.

An iris image may be acquired at block 21 according to any methodsuitable for the purpose. For example, an image may be acquired using aconventional camera. In some embodiments, a suitable sensor may beemployed. In still further embodiments, images may be acquired from avideo stream. In embodiments, an image may be obtained of an eye, andthe iris region of the image of the eye may be extracted or segmentedtherefrom.

In various embodiments, an iris image may be acquired using light at asuitable wavelength or wavelength range. For example, a wavelength rangeof 700-900 nanometer (nm) (near-infrared illumination) may be suitable.The acquisition system may also vary in its obtrusiveness to thesubject. In various embodiments, for example, a subject may be promptedto position the eye at a certain spatial orientation for focusing and/orfor obtaining an iris image of a certain size. In other embodiments,however, an acquisition system may be of a less obtrusive nature,actively locating and acquiring an image of any eye in a certain spatialrelation to the camera (or other acquisition device).

After acquiring one or more images of a subject's eye, the iris regionof the eye images may be segmented for analysis. Segmentation refers tolocating that part of the acquired image that corresponds to the irisregion. Locating the iris may be based upon assumptions regarding thegeneral shape and/or location of the eye/iris relative to other featuresof the face/eye. According to various embodiments, the pupillary andlimbic boundaries may be approximated as circles such that a boundarymay be described in terms of radius, r, and circle coordinates, x₀ andy₀. An integro-differential operator may be used for detecting the irisboundary by searching the parameter space. An exemplaryintegro-differential operator is:

${\max \left( {r,x_{0},y_{0}} \right)}{{{{G\sigma}(r)}\mspace{11mu}*\mspace{11mu} \frac{\partial}{\partial r}{\oint\limits_{r,x_{0},y_{0}}{\frac{1\left( {x,y} \right)}{2\pi \; r}{s}}}}}$

where Gσ(r) is a smoothing function and I(x,y) is the image of the eye.It is important to note that the disclosed invention is not limited tothe foregoing segmentation method. Any number of various othersegmentation methods may be similarly suitable.

It is known that the pupillary and limbic boundaries are not alwaysperfectly circular. Furthermore, noise may be introduced by way ofocclusion by eyelids, eyelashes, and/or specularities. Accordingly,alternative segmentation methods may be employed to better model theiris boundaries.

Once the iris has been located, the iris texture may be analyzed atblock 22 to obtain one or more feature vectors. Iris texture may beanalyzed and represented according to one or more of various approaches.In various embodiments, the binary feature vector method may be used toextract the textural features of the iris(es), which includes obtaininga normalized iris image to account for differences in iris sizes acrosssubjects, and displaying the normalized image, for example, inrectangular form, with a radial coordinate (a value between 0 and 1) onthe vertical axis, and an angular coordinate (a value between 0 and 360degrees) on the horizontal axis. Accordingly, the pupillary boundary ofthe iris is along the bottom of the normalized image, and the limbicboundary is along the top. Further, the left side of the normalizedimage marks 0 degrees on the iris image, and the right side marks 360degrees.

In an embodiment, using convolution with 2-dimensional Gabor filtersallows for extraction of the texture from the normalized image. Complexcoefficients are generated by multiplying the filters by the pixel dataof the raw image. In an embodiment, the values representing the complexcoefficients may then be binarized. In an embodiment, the complexcoefficients may be transformed into a two-bit code, the first bitrepresenting the real part of the coefficient and the second bitrepresenting the imaginary part of the coefficient. In an alternateembodiment, only part of the complex coefficients may be binarized, oronly one bit may be generated from the complex value. In an embodiment,after analyzing the image using the Gabor filters, the information fromthe iris images may be summarized, for example, in a 256 byte (2048 bit)binary code, which may be compared efficiently using bitwise operationsduring an authentication operation (i.e., matching of the enrollmenttemplate with the sample image).

Other methods may be similarly suitable for analyzing and representingthe iris texture. For example, another method for binary representationmay be used instead of the method discussed above. Alternatively, theiris texture may be represented by using a real-valued feature vector.In still other embodiments, some combination of binary and real-valuedfeature vectors may be employed. In still further embodiments, acomplex-valued feature vector may be used, or some other representationthat represents the iris texture in a manner allowing for adetermination of consistency (as discussed more fully herein) acrossdifferent representations.

Alignment of feature vectors may be performed at block 23 in situationsin which multiple images of an eye have differences in orientation.Unsurprisingly, multiple images of an eye may not necessarily have thesame orientation due, for example, to small movements of the eye (or ofthe body) that tend to occur even over small intervals of time.Specifically, if a head is tilted in one image and upright in a secondimage, the feature vector extracted from the first image may be ashifted version of the feature vector extracted from the second image.Accordingly, in an embodiment, feature vectors may be aligned so thatall feature vectors correspond to the same orientation of the subjectiris.

In various embodiments, feature vectors may be aligned by taking a firstacquired image as a reference. Feature vectors of other acquired imagesmay then be compared to the feature vector of the first acquired image(first feature vector) at multiple possible shifts. For each shift, adistance measure may be computed between the first feature vector and aparticular other feature vector (second feature vector). The shiftcorresponding to the smallest possible distance may be taken to be tothe correct orientation for the second feature vector.

In some embodiments, the second feature vector may be aligned to thefirst feature vector, and a third feature vector of yet another acquiredimage may be compared to the first and the second feature vectors. Indetermining the correct orientation for the third vector, the thirdvector may be compared to both the first and the secondpreviously-aligned vectors. Other feature vectors may be compared tosome or all of previous feature vectors in determining the optimalshift. It is noted that as used here, “first,” “second,” and “third” donot necessarily refer to first, second, or third in a sequence. Thefirst, second, or third acquired images may, for example, be any one ofa series of acquired images chosen, selectively or at random, foralignment.

As noted herein, in various embodiments multiple images may be acquiredfrom a video stream. In the embodiments, information from the videostream may be used for aligning feature vectors.

Other suitable alignment methods may be employed for aligning featurevectors or iris images. In some embodiments, alignment may be excludedaltogether as desired.

Given multiple feature vectors, which may or may not be aligned, thefeature vectors may be compared and analyzed for consistency at block24. For embodiments in which feature vectors are represented in binaryform, an average of each bit in the feature vector may be determined. Ifthe average for a given bit is within a predetermined distance (athreshold) to either 0 or 1, then the bit may be considered consistent.The maximum inconsistency, then, would be 0.5, the mid-point between 0and 1.

FIG. 3 illustrates exemplary inconsistent regions of iris featurevectors of five different test subjects. For each of the test subjects,a plurality of images were acquired of the subject's iris, and a binaryfeature vector was obtained for each image, resulting in a plurality ofbinary feature vectors for each subject's iris. The plurality of binaryfeature vectors were aligned and compared. In the illustrated featurevectors, the black regions correspond to inconsistent regions, based ona threshold inconsistency value of 30%. In this embodiment, the 30%value refers to both a bit from the feature vector being equal to 1 forsome of the images but 0 for at least 30% of the images, and a bit fromthe feature vector being equal to 0 for some of the images but 1 for atleast 30% of the images. Here, the 30% threshold is merely exemplary,and so, in various other embodiments, inconsistency may be defined atsome value more or less than 30%.

Although a binary feature vector system may permit fast comparisonsbetween feature vectors, other non-binary feature vectors may beemployed within the scope of embodiments of the present invention. Insome of these embodiments, an average and a standard deviation of eachelement of the feature vector may be determined. Elements with a highstandard deviation (or a standard deviation outside of an acceptabilitywindow) may be deemed inconsistent. In still further embodiments, othermeasures of inconsistency may be employed.

Having made a determination of the consistency or inconsistency of oneor more bits of a feature vector, the consistent and/or inconsistentbits may be variously treated in generating an enrollment template. Forexample, in various embodiments and as depicted at query block 25 ofFIG. 2, inconsistent bits may be masked or otherwise ignored whenconstructing an enrollment template at block 26 so that decisionsregarding the identity of a subject may be based solely on the mostconsistent parts of the iris feature vector. In various ones of theseembodiments, a threshold value, τ, may be defined, where 0<τ<0.5. If,for example, τ is set to 0.4, any bit with an average value greater thanτ and less than 1−τ is masked with a consistency mask (i.e., valuesbetween 0.4 and 0.6 are masked). Needless to say, varying the thresholdvalue may affect the amount of information masked as evident in FIG. 4.For the feature vector depicted in FIG. 4, black regions correspond toinconsistent bits as defined by a predetermined threshold value. At 42,the feature vector was masked by a threshold value of τ=0.2; at 44, by athreshold value of τ=0.3; and at 46, by a threshold value of τ=0.4. Asillustrated, as the threshold value increases, so too does the number ofbits masked. Accordingly, in an embodiment, an appropriate level ofoptimization may be desired, depending on the application.

In various embodiments, a threshold value, τ, may be predetermined andkept constant for a plurality of subjects. In various other embodiments,however, the threshold value may be varied for each subject. Forexample, the threshold value may be set for each subject so that acertain percentage of the feature vector remains unmasked by theconsistency mask. This embodiment may be desirable to retain a minimumamount of information for subsequent comparison.

In alternate embodiments, rather than masking inconsistent features, itmay be preferred to weight features based on their consistency at block27 of FIG. 2. In various ones of these embodiments, parts of an irisfeature vector may be weighted as depicted at block 28 and used forconstructing an enrollment template at block 29 so that more consistentparts of the feature vector are given more weight in comparisons(authentication) relative to less consistent parts of the featurevector.

In embodiments, one or more other masks in addition to a consistencymask and/or weighting may be included in an enrollment template for asubject. For example, in various embodiments, an occlusion mask may beincluded in an enrollment template to account for occlusion by eyelids,eyelashes, and/or specularities.

Turning now to FIG. 5, illustrated is a flow diagram of a portion of theoperations associated with authentication operations for verifying theidentity of a subject. An enrollment template, including any maskingand/or weighting, may be used for verifying the identity of a subject.In various embodiments, the enrollment template may be one constructedaccording to the method described with reference to FIG. 2. In general,during authentication, the enrollment template is compared to a samplefeature vector, the sample feature vector acquired for a subjectpurporting to have the identity corresponding to the enrollmenttemplate. The enrollment template may be one created prior to or duringone or more authentication operations, depending on the application.

A sample iris image may be obtained at block 51 of FIG. 5 according toany method suitable for the purpose and may be a method similar to onedescribed with reference to enrollment template generation, describedherein. For example, an image may be one acquired using a conventionalcamera. In some embodiments, a suitable sensor may be employed. In stillfurther embodiments, images may be acquired from a video stream. Invarious embodiments, the sample iris image may be one that has alreadybeen acquired, the sample iris image being in a form substantially readyfor use in one or more authentication operations including, for example,analysis of the sample iris image for consistent/inconsistent featuresand/or comparison to another image (e.g., an enrolled image).

A sample feature vector may be obtained for the sample iris at block 52.The sample feature vector may be obtained using any method suitable forthe purpose and may be a method similar to one described with referenceto enrollment template generation, described herein. For example, asample feature vector may be a binary feature vector. Alternatively, asample feature vector may be a real-valued feature vector. In stillother embodiments, some combination of binary and real-valued featurevectors may be employed. In still further embodiments, a complex-valuedfeature vector may be used, or some other representation that representsthe sample iris texture in a manner allowing for a determination ofconsistency (as discussed more fully herein) across differentrepresentations.

An enrollment template may be compared to a sample feature vector atblock 53 to determine whether the enrollment template and the samplematch at query block 54. In various embodiments, a comparison may bemade between a sample feature vector and an enrolled feature vector ofthe enrollment template. Any suitable method may be used for thecomparing. For example, in various embodiments, the bits of binaryfeature vectors may be compared according to the normalized Hammingdistance. The normalized Hamming distance generally refers to thefraction of bits that differ between binary feature vectors (i.e., thebits that disagree).

According to various embodiments, the Boolean logic representation forfinding the bits that differ between two binary feature vectors may bethe exclusive OR (⊕) function. Accordingly, bits in a feature vectorthat are unoccluded (if an occlusion mask is included in the enrollmentfeature vector) and consistent (if a consistency mask is included in theenrollment feature vector) may be represented by a logic 1 in theocclusion mask and consistency mask, respectively. An intersectionoperation (∩) may be used to find the bits that are unoccluded (if anocclusion mask is included in the enrollment feature vector) andconsistent (if a consistency mask is included in the enrollment featurevector) in both the enrolled feature vector and the sample featurevector. Accordingly, the fraction of consistent, unoccluded bits thatdisagree between the enrolled feature vector and the sample featurevector may be determined according to the following algorithm:

$\frac{{{}\left( {{F\; V_{A}} \oplus {F\; V_{B}}} \right)}\bigcap{O\; M_{A}}\bigcap{O\; M_{B}}\bigcap{C\; M_{A}{}}}{{{}{OM}_{A}}\bigcap{OM}_{B}\bigcap{{CM}_{A}{}}}$

where FV_(A) refers to the enrolled feature vector; FV_(B) refers to thesample feature vector; OM_(A) refers to the occlusion mask for theenrolled feature vector; CM_(A) refers to the consistency mask for theenrolled feature vector; and OM_(B) refers to the occlusion mask for thesample feature vector.

Notice in the foregoing embodiment that the sample feature vectorincludes an occlusion mask. Such a mask may be included, in variousembodiments, during the comparison operation to account for occlusion byeyelids, eyelashes, and/or specularities of the sample taken from thesubject purporting to have the identity corresponding to the enrollmenttemplate.

Rather than separating the consistency mask and the occlusion mask aswas done in the foregoing embodiment, the masks may be combined into onevector in various embodiments. Combining the masks into one vector mayreduce storage volume of enrollment data and/or processing time for theauthentication.

As noted previously, parts of an iris feature vector may be weighted sothat the more consistent parts of the feature vector are given moreweight relative to less consistent parts of the feature vector. Invarious embodiments in which binary feature vectors are used, theaverage value for each bit may be stored in an array, Avg. If a bit isless than 0.5, then the consistency weights vector, CW, for that bit maybe computed to be 2*(0.5−(average value for bit)). Otherwise, if the bitis greater than or equal to 0.5, CWfor that bit may be computed to be2*((average value for bit)−0.5). This algorithm may be an iterativefunction represented as:

for i=1 to |Avg| if Avg[i] < 0.5 then CW[i] = 2*(0.5−Avg[i]) else CW[i]= 2*(Avg[i] −0.5) endIn this embodiment, the distance between the enrollment feature vectorand the sample feature vector may be determined according to thefollowing algorithm:

$\frac{\sum\limits_{i}{{\left( {{F\; V_{A}} \oplus {F\; V_{B}}} \right)\lbrack i\rbrack} \cdot {\left( {{O\; M_{A}}\bigcap{O\; M_{B}}} \right)\lbrack i\rbrack} \cdot {{CW}\lbrack i\rbrack}}}{\sum\limits_{i}{{\left( {{O\; M_{A}}\bigcap{O\; M_{B}}} \right)\lbrack i\rbrack} \cdot {{CW}\lbrack i\rbrack}}}$

Other methods may be employed for an authentication operation inaddition to or alternately to the foregoing methods. For example, insome embodiments, consistency information may be calculated from imagesor feature vectors from a sample subject rather than an enrolledsubject. In other embodiments, consistency information may be calculatedfrom images or feature vectors from both the sample subject and enrolledsubject.

According to various embodiments, if the enrolled feature vector and thesample feature vector match, an indication of identity acceptance may beprovided at block 55. Otherwise, an indication of identity rejection maybe provided at block 56 if the enrolled feature vector and the samplefeature vector do not match. In determining whether feature vectorsmatch, any criteria suitable for the purpose may be employed. Forexample, a determination of a match may be made if a predeterminedpercentage (e.g., 70%, or some other percentage) of bits of binaryfeature vectors match. In embodiments, the determination of identityrejection may prompt one or more resulting actions, such as requiring afurther proffer of identity (such as other biometric indicia includingfingerprints, voice, etc., or other form of identification), orproviding notification or alarm to an individual or to a device.

As noted previously, a biometric method may sometimes comprise one ormore identification operations for identifying an unknown subject.During identification, a sample may be acquired from the unknown subjectand matched against one or more enrolled subjects to identify theunknown subject.

Illustrated in FIG. 6 is a portion of the operations associated with anexemplary identification method. An enrollment template, including anymasking and/or weighting, may be used for identifying an unknownsubject. In various embodiments, the enrollment template may be oneconstructed according to the method described with reference to FIG. 2.In general, during identification, a sample from the unknown subject maybe compared against one or more enrolled subjects. The enrollmenttemplate may be one created prior to or during one or moreidentification operations, depending on the application.

A sample iris image may be obtained at block 61 of FIG. 6 according toany method suitable for the purpose and may be a method similar to onedescribed with reference to enrollment template generation such asdescribed herein. For example, an image may be one acquired using aconventional camera. In some embodiments, a suitable sensor may beemployed. In still further embodiments, images may be acquired from avideo stream. In various embodiments, the sample iris image may be onethat has already been acquired, the sample iris image being in a formsubstantially ready for use in one or more identification operationsincluding, for example, analysis of the sample iris image forconsistent/inconsistent features and/or comparison to another image(e.g., an enrolled image).

A sample feature vector may be obtained for the sample iris at block 62.The sample feature vector may be obtained using any method suitable forthe purpose and may be a method similar to one described with referenceto enrollment template generation described herein. For example, asample feature vector may be a binary feature vector. Alternatively, asample feature vector may be a real-valued feature vector. In stillother embodiments, some combination of binary and real-valued featurevectors may be employed. In still further embodiments, a complex-valuedfeature vector may be used, or some other representation that representsthe sample iris texture in a manner allowing for a determination ofconsistency (as discussed more fully herein) across differentrepresentations.

An enrollment template may be compared to a sample feature vector atblock 63 to determine whether the enrollment template and the samplematch at query block 64. In various embodiments, a comparison may bemade between a sample feature vector and an enrolled feature vector ofthe enrollment template. Any suitable method may be used for thecomparing. For example, in various embodiments, the bits of binaryfeature vectors may be compared according to the normalized Hammingdistance, described herein. Comparison between the sample feature vectorand the enrollment template may include any one or more of variouscomparison operations as described above with reference toauthentication, with or without the various masks.

Other methods may be employed for an identification operation inaddition to or alternately to the foregoing methods. For example, insome embodiments, consistency information may be calculated from imagesor feature vectors from a sample subject rather than an enrolledsubject. In other embodiments, consistency information may be calculatedfrom images or feature vectors from both the sample subject and enrolledsubject.

According to various embodiments, if the enrolled feature vector and thesample feature vector match, an indication of the match may be providedat block 65. Otherwise, an indication of a non-match may be provided atblock 66 if the enrolled feature vector and the sample feature vector donot match. In determining whether feature vectors match, any criteriasuitable for the purpose may be employed. For example, a determinationof a match may be made if a predetermined percentage (e.g., 70%, or someother percentage) of bits of binary feature vectors match. Inembodiments, the determination of a non-match may prompt one or moreresulting actions, such as requiring a further proffer of identity (suchas other biometric indicia including fingerprints, voice, etc., or otherform of identification), or providing notification or alarm to anindividual or to a device.

Turning now to FIG. 7, an iris recognition apparatus 700 may beconfigured to perform any one or more of various embodiments, in part orin whole, as discussed herein. In the illustrated embodiment, irisrecognition apparatus 700 may comprise a sensor 72, an enrollmenttemplate generator 74, and an authenticator 66.

Sensor 72 may be configured to acquire a plurality of images. Accordingto various embodiments, sensor 72 may be configured to acquire imagesduring an enrollment operation and/or during an authenticationoperation. Sensor 72 may be a camera or similar device. In someembodiments, sensor 72 may be configured to capture one or more of stilland moving images, depending on the application.

Enrollment template generator 74 may be configured to perform one ormore enrollment operations as described herein. For example, in variousembodiments, enrollment template generator 74 may be configured to aligna plurality of images to an orientation of an iris. In variousembodiments, enrollment template generator 74 may be configured tocompare a plurality of images to determine one or more consistentfeatures and one or more inconsistent features of an enrolled iris. Instill further embodiments, enrollment template generator 74 may beconfigured to construct an enrollment template for an enrolled irisbased at least in part on the one or more consistent features and theone or more inconsistent features. The constructed enrollment templatemay, in embodiments, include one or more masks and/or one or more weightvalues.

Authenticator 76 may be configured to perform one or more authenticationoperations as described herein. For example, authenticator 76 may beconfigured to compare a sample image with an enrollment template todetermine whether the sample iris and the enrolled iris match.

Iris recognition apparatus 700 may be further adapted to store variousinformation associated with iris recognition. For instance, irisrecognition apparatus 700 may be adapted to store one or more ofparameters, instructions, and enrollment templates for performing one ormore methods as disclosed herein.

Any one or more of various embodiments as previously discussed may beincorporated, in part or in whole, into an article of manufacture. Invarious embodiments and as shown in FIG. 8, an article of manufacture800 in accordance with various embodiments of the present invention maycomprise a storage medium 82 and a plurality of programming instructions82 stored in storage medium 82. In various ones of these embodiments,programming instructions 84 may be adapted to program an apparatus toenable the apparatus to perform one or more of the previously-discussedmethods. For example, programming instructions 84 may be adapted toprogram an apparatus to enable the apparatus to perform enrollmentand/or authentication operations as described herein.

Although certain embodiments have been illustrated and described hereinfor purposes of description of the preferred embodiment, it will beappreciated by those of ordinary skill in the art that a wide variety ofalternate and/or equivalent embodiments or implementations calculated toachieve the same purposes may be substituted for the embodiments shownand described without departing from the scope of the present invention.Those with skill in the art will readily appreciate that embodiments inaccordance with the present invention may be implemented in a very widevariety of ways. This application is intended to cover any adaptationsor variations of the embodiments discussed herein. Therefore, it ismanifestly intended that embodiments in accordance with the presentinvention be limited only by the claims and the equivalents thereof.

1. An iris recognition method comprising: comparing a plurality ofimages of an iris; and determining at least one of one or moreconsistent features and one or more inconsistent features of the iris.2. The method of claim 1, further comprising forming a feature vectorfor each of the plurality of images, and wherein said comparingcomprises comparing the feature vectors to determine the at least one ofthe one or more consistent features and the one or more inconsistentfeatures of the iris.
 3. The method of claim 2, wherein said forming thefeature vector comprises forming a binary feature vector for each of theplurality of images.
 4. The method of claim 2, wherein said forming thefeature vector comprises forming a real-valued or complex-valued featurevector for each of the plurality of images.
 5. The method of claim 2,further comprising aligning the feature vectors to an orientation of theiris.
 6. The method of claim 1, wherein said comparing comprisesidentifying the at least one of the one or more consistent features andthe one or more inconsistent features based at least in part on aconsistency threshold.
 7. The method of claim 1, further comprisingconstructing an enrollment template for the iris based at least in parton said comparing.
 8. The method of claim 7, wherein the enrollmenttemplate includes at least one of a consistency mask to mask the one ormore inconsistent features and an occlusion mask to mask one or moreocclusions of the iris.
 9. The method of claim 7, wherein saidconstructing the enrollment template comprises assigning a weight valuefor each of the at least one of the one or more consistent features andthe one or more inconsistent features based at least in part on aconsistency value corresponding to each of the features.
 10. An irisrecognition method comprising: obtaining an enrollment templatecorresponding to an enrolled iris, the enrollment template includinginformation based at least in part on at least one of one or moreconsistent features and one or more inconsistent features of theenrolled iris; and comparing at least one sample image of a sample iriswith the enrollment template to determine whether the sample iris andthe enrolled iris match.
 11. The method of claim 10, wherein saidobtaining the enrollment template comprises: comparing a plurality ofimages of an iris to be enrolled to determine at least one of one ormore consistent features and one or more inconsistent features of theiris; and constructing the enrollment template for the enrolled irisbased at least in part on said comparing the plurality of images of theenrolled iris.
 12. The method of claim 11, further comprising obtainingthe at least one sample image, and wherein said obtaining the enrollmenttemplate is performed during or after said obtaining the at least onesample image.
 13. The method of claim 10, further comprising: comparinga plurality of sample images of the sample iris to determine at leastone of one or more consistent features and one or more inconsistentfeatures of the sample iris; and wherein said comparing the at least onesample image of the sample iris comprises comparing the enrollmenttemplate with information based at least in part on the at least one ofone or more consistent features and the one or more inconsistentfeatures of the sample iris.
 14. The method of claim 10, furthercomprising providing an indication of identity acceptance of the sampleiris if it is determined that the sample iris and the enrolled irismatch.
 15. The method of claim 10, further comprising providing anindication of a match it is determined that the sample iris and theenrolled iris match.
 16. The method of claim 10, further comprisingforming a sample feature vector for the sample image, and wherein saidcomparing comprises comparing the sample feature vector with theenrollment template to determine whether the sample iris and theenrolled iris match.
 17. The method of claim 10, further comprisingmasking one or more occlusions of the sample iris.
 18. The method ofclaim 10, wherein the enrollment template is configured to mask the oneor more inconsistent features of the enrolled iris.
 19. The method ofclaim 10, wherein the enrollment template includes a weight value foreach of the one or more consistent features and one or more inconsistentfeatures based at least in part on a consistency value corresponding toeach of the features.
 20. An iris recognition apparatus comprising: asensor configured to acquire a plurality of images of an iris; and anenrollment template generator configured to compare the plurality ofimages to determine one or more consistent features and one or moreinconsistent features of the iris, and to construct an enrollmenttemplate for the iris based at least in part on the one or moreconsistent features and the one or more inconsistent features, whereinthe iris for which an enrollment template has been constructed is termedan enrolled iris.
 21. The apparatus of claim 20, wherein the sensor isfurther configured to acquire a sample image of a sample iris.
 22. Theapparatus of claim 21, further comprising an authenticator configured tocompare the sample image with the enrollment template to determinewhether the sample iris and the enrolled iris match.
 23. The apparatusof claim 20, wherein the enrollment template generator is furtherconfigured to align the plurality of images to an orientation of theiris.
 24. The apparatus of claim 20, wherein the enrollment template isconfigured to mask the one or more inconsistent features.
 25. Theapparatus of claim 20, wherein the enrollment template generator isfurther configured to assign a weight value for each of the one or moreconsistent features and one or more inconsistent features based at leastin part on a consistency value corresponding to each of the features.